Overview

Dataset statistics

Number of variables7
Number of observations17472
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory64.0 B

Variable types

DateTime2
TimeSeries5

Timeseries statistics

Number of series5
Time series length17472
Starting point2022-01-01 00:00:00
Ending point2023-12-30 23:00:00
Period1 hour and 5.89 seconds
2024-05-06T09:39:40.544305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:41.103650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Alerts

dhi is highly overall correlated with dni and 1 other fieldsHigh correlation
dni is highly overall correlated with dhi and 1 other fieldsHigh correlation
ghi is highly overall correlated with dhi and 1 other fieldsHigh correlation
temp is non stationaryNon stationary
wind_spd is non stationaryNon stationary
dhi is non stationaryNon stationary
ghi is non stationaryNon stationary
dni is non stationaryNon stationary
temp is seasonalSeasonal
wind_spd is seasonalSeasonal
dhi is seasonalSeasonal
ghi is seasonalSeasonal
dni is seasonalSeasonal
timestamp_utc has unique valuesUnique
dhi has 8632 (49.4%) zerosZeros
ghi has 8672 (49.6%) zerosZeros
dni has 8654 (49.5%) zerosZeros

Reproduction

Analysis started2024-05-06 07:39:32.030265
Analysis finished2024-05-06 07:39:40.307004
Duration8.28 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct17470
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size273.0 KiB
Minimum2022-01-01 00:00:00
Maximum2023-12-30 23:00:00
2024-05-06T09:39:41.642901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:41.823131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

timestamp_utc
Date

UNIQUE 

Distinct17472
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size273.0 KiB
Minimum2021-12-31 23:00:00
Maximum2023-12-30 22:00:00
2024-05-06T09:39:42.008918image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:42.206345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temp
Numeric time series

NON STATIONARY  SEASONAL 

Distinct322
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.098094
Minimum-0.5
Maximum34.7
Zeros0
Zeros (%)0.0%
Memory size273.0 KiB
2024-05-06T09:39:42.467379image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile6.7
Q112.7
median18
Q324.025
95-th percentile28.8
Maximum34.7
Range35.2
Interquartile range (IQR)11.325

Descriptive statistics

Standard deviation6.9540729
Coefficient of variation (CV)0.38424338
Kurtosis-0.9045947
Mean18.098094
Median Absolute Deviation (MAD)5.6
Skewness-0.078815103
Sum316209.9
Variance48.35913
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.1805122332
2024-05-06T09:39:42.727000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-06T09:39:44.185910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-06T09:39:44.448640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
14.7 217
 
1.2%
15 196
 
1.1%
26 194
 
1.1%
12.2 190
 
1.1%
14.2 188
 
1.1%
14 178
 
1.0%
13.2 176
 
1.0%
14.5 172
 
1.0%
15.5 170
 
1.0%
12.5 168
 
1.0%
Other values (312) 15623
89.4%
ValueCountFrequency (%)
-0.5 2
 
< 0.1%
-0.2 1
 
< 0.1%
0.1 1
 
< 0.1%
0.2 1
 
< 0.1%
0.5 5
< 0.1%
0.7 1
 
< 0.1%
1 3
< 0.1%
1.1 1
 
< 0.1%
1.5 2
 
< 0.1%
1.6 4
< 0.1%
ValueCountFrequency (%)
34.7 2
< 0.1%
34.4 1
 
< 0.1%
34.2 1
 
< 0.1%
34 1
 
< 0.1%
33.8 1
 
< 0.1%
33.7 2
< 0.1%
33.6 3
< 0.1%
33.5 1
 
< 0.1%
33.4 1
 
< 0.1%
33.1 2
< 0.1%
2024-05-06T09:39:43.852959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

wind_spd
Numeric time series

NON STATIONARY  SEASONAL 

Distinct133
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1028028
Minimum0
Maximum15.9
Zeros1
Zeros (%)< 0.1%
Memory size273.0 KiB
2024-05-06T09:39:44.804561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12.6
median3.6
Q35.09
95-th percentile8.19
Maximum15.9
Range15.9
Interquartile range (IQR)2.49

Descriptive statistics

Standard deviation2.1834863
Coefficient of variation (CV)0.53219382
Kurtosis1.8332907
Mean4.1028028
Median Absolute Deviation (MAD)1
Skewness1.0922296
Sum71684.17
Variance4.7676124
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.78182106 × 10-23
2024-05-06T09:39:45.068161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-06T09:39:46.101666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-06T09:39:46.287574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3.6 2001
11.5%
3.1 1935
11.1%
4.09 1751
10.0%
2.6 1561
8.9%
4.59 1442
8.3%
2.1 1384
 
7.9%
5.09 1214
 
6.9%
1 935
 
5.4%
6.2 851
 
4.9%
1.5 847
 
4.8%
Other values (123) 3551
20.3%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.25 1
 
< 0.1%
0.33 1
 
< 0.1%
0.4 12
 
0.1%
0.5 203
 
1.2%
0.72 1
 
< 0.1%
0.75 1
 
< 0.1%
0.8 13
 
0.1%
0.87 1
 
< 0.1%
1 935
5.4%
ValueCountFrequency (%)
15.9 1
 
< 0.1%
15.4 2
 
< 0.1%
14.4 8
 
< 0.1%
13.9 9
 
0.1%
13.4 23
0.1%
12.9 25
0.1%
12.4 3
 
< 0.1%
12.3 32
0.2%
11.85 1
 
< 0.1%
11.8 31
0.2%
2024-05-06T09:39:45.687624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

dhi
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct124
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.858288
Minimum0
Maximum123
Zeros8632
Zeros (%)49.4%
Memory size273.0 KiB
2024-05-06T09:39:46.758489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.5
Q391
95-th percentile118
Maximum123
Range123
Interquartile range (IQR)91

Descriptive statistics

Standard deviation47.37767
Coefficient of variation (CV)1.1054494
Kurtosis-1.5564483
Mean42.858288
Median Absolute Deviation (MAD)8.5
Skewness0.41366257
Sum748820
Variance2244.6436
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9044002042
2024-05-06T09:39:47.036764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-06T09:39:48.475952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-06T09:39:48.710953image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 8632
49.4%
122 232
 
1.3%
117 218
 
1.2%
121 216
 
1.2%
109 170
 
1.0%
118 164
 
0.9%
110 162
 
0.9%
116 156
 
0.9%
99 156
 
0.9%
112 152
 
0.9%
Other values (114) 7214
41.3%
ValueCountFrequency (%)
0 8632
49.4%
1 12
 
0.1%
2 12
 
0.1%
3 12
 
0.1%
4 18
 
0.1%
5 8
 
< 0.1%
6 18
 
0.1%
7 10
 
0.1%
8 14
 
0.1%
9 20
 
0.1%
ValueCountFrequency (%)
123 140
0.8%
122 232
1.3%
121 216
1.2%
120 120
0.7%
119 136
0.8%
118 164
0.9%
117 218
1.2%
116 156
0.9%
115 124
0.7%
114 110
0.6%
2024-05-06T09:39:48.044026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

ghi
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct983
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.08883
Minimum0
Maximum991
Zeros8672
Zeros (%)49.6%
Memory size273.0 KiB
2024-05-06T09:39:49.125660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q3437
95-th percentile878
Maximum991
Range991
Interquartile range (IQR)437

Descriptive statistics

Standard deviation308.20304
Coefficient of variation (CV)1.3279529
Kurtosis-0.33968156
Mean232.08883
Median Absolute Deviation (MAD)3
Skewness1.0316001
Sum4055056
Variance94989.115
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.6957092143
2024-05-06T09:39:49.387740image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-06T09:39:50.475582image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-06T09:39:50.738340image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 8672
49.6%
64 32
 
0.2%
370 32
 
0.2%
894 30
 
0.2%
1 30
 
0.2%
869 28
 
0.2%
191 28
 
0.2%
2 28
 
0.2%
770 28
 
0.2%
991 26
 
0.1%
Other values (973) 8538
48.9%
ValueCountFrequency (%)
0 8672
49.6%
1 30
 
0.2%
2 28
 
0.2%
3 24
 
0.1%
4 12
 
0.1%
5 16
 
0.1%
6 18
 
0.1%
7 18
 
0.1%
8 10
 
0.1%
9 16
 
0.1%
ValueCountFrequency (%)
991 26
0.1%
990 14
0.1%
989 10
 
0.1%
988 8
 
< 0.1%
987 6
 
< 0.1%
986 6
 
< 0.1%
985 6
 
< 0.1%
984 6
 
< 0.1%
983 4
 
< 0.1%
982 4
 
< 0.1%
2024-05-06T09:39:50.038271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

dni
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct805
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.05357
Minimum0
Maximum924
Zeros8654
Zeros (%)49.5%
Memory size273.0 KiB
2024-05-06T09:39:51.142412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26.5
Q3767
95-th percentile903
Maximum924
Range924
Interquartile range (IQR)767

Descriptive statistics

Standard deviation380.44115
Coefficient of variation (CV)1.0899219
Kurtosis-1.7101447
Mean349.05357
Median Absolute Deviation (MAD)26.5
Skewness0.32548006
Sum6098664
Variance144735.47
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9073866562
2024-05-06T09:39:51.425412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
2024-05-06T09:39:52.420491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Gap statistics

number of gaps1
min1 day and 1 hour
max1 day and 1 hour
mean1 day and 1 hour
std0
2024-05-06T09:39:52.718250image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 8654
49.5%
893 98
 
0.6%
915 88
 
0.5%
914 80
 
0.5%
857 62
 
0.4%
920 62
 
0.4%
870 60
 
0.3%
913 54
 
0.3%
919 54
 
0.3%
892 52
 
0.3%
Other values (795) 8208
47.0%
ValueCountFrequency (%)
0 8654
49.5%
1 10
 
0.1%
2 4
 
< 0.1%
3 8
 
< 0.1%
5 12
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
11 2
 
< 0.1%
ValueCountFrequency (%)
924 38
0.2%
923 42
0.2%
922 28
 
0.2%
921 24
 
0.1%
920 62
0.4%
919 54
0.3%
918 40
0.2%
917 38
0.2%
916 30
 
0.2%
915 88
0.5%
2024-05-06T09:39:52.026356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ACF and PACF

Interactions

2024-05-06T09:39:39.508707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:36.817054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:37.509314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:38.207604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:38.944646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:39.618728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:36.939815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:37.647343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:38.340883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:39.110421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:39.726860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:37.074610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:37.791686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:38.527360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:39.229980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:39.839408image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:37.232972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:37.944201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:38.656850image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:39.327860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:39.926597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:37.407825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:38.078457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:38.818856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-06T09:39:39.422325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-06T09:39:52.907133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
dhidnighitempwind_spd
dhi1.0000.9990.9990.3850.330
dni0.9991.0000.9990.3800.330
ghi0.9990.9991.0000.3920.328
temp0.3850.3800.3921.000-0.001
wind_spd0.3300.3300.328-0.0011.000

Missing values

2024-05-06T09:39:40.039762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-06T09:39:40.192631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timestamp_localtimestamp_utctempwind_spddhighidni
2022-01-01 00:00:002022-01-01 00:00:002021-12-31 23:00:0010.71.0000
2022-01-01 01:00:002022-01-01 01:00:002022-01-01 00:00:0010.72.6000
2022-01-01 02:00:002022-01-01 02:00:002022-01-01 01:00:0010.72.6000
2022-01-01 03:00:002022-01-01 03:00:002022-01-01 02:00:0010.22.6000
2022-01-01 04:00:002022-01-01 04:00:002022-01-01 03:00:0010.51.5000
2022-01-01 05:00:002022-01-01 05:00:002022-01-01 04:00:0010.51.5000
2022-01-01 06:00:002022-01-01 06:00:002022-01-01 05:00:0010.52.6000
2022-01-01 07:00:002022-01-01 07:00:002022-01-01 06:00:0010.01.0000
2022-01-01 08:00:002022-01-01 08:00:002022-01-01 07:00:008.01.5000
2022-01-01 09:00:002022-01-01 09:00:002022-01-01 08:00:008.61.03964340
timestamp_localtimestamp_utctempwind_spddhighidni
2023-12-30 14:00:002023-12-30 14:00:002023-12-30 13:00:0015.22.1087382745
2023-12-30 15:00:002023-12-30 15:00:002023-12-30 14:00:0014.23.6079298686
2023-12-30 16:00:002023-12-30 16:00:002023-12-30 15:00:0014.03.6062176559
2023-12-30 17:00:002023-12-30 17:00:002023-12-30 16:00:0013.24.093241259
2023-12-30 18:00:002023-12-30 18:00:002023-12-30 17:00:0012.23.60000
2023-12-30 19:00:002023-12-30 19:00:002023-12-30 18:00:0011.22.10000
2023-12-30 20:00:002023-12-30 20:00:002023-12-30 19:00:0010.72.60000
2023-12-30 21:00:002023-12-30 21:00:002023-12-30 20:00:0010.73.10000
2023-12-30 22:00:002023-12-30 22:00:002023-12-30 21:00:0010.64.59000
2023-12-30 23:00:002023-12-30 23:00:002023-12-30 22:00:007.71.00000